Mobile Robot Destination Generation by Tracking a Remote Controller Using a Vision-aided Inertial Navigation Algorithm

A new remote control algorithm for a mobile robot is proposed, where a remote controller consists of a camera and inertial sensors. Initially the relative position and orientation of a robot is estimated by capturing four circle landmarks on the plate of the robot. When the remote controller moves to point to the destination, the camera pointing trajectory is estimated using an inertial navigation algorithm. The destination is transmitted wirelessly to the robot and then the robot is controlled to move to the destination. A quick movement of the remote controller is possible since the destination is estimated using inertial sensors. Also unlike the vision only control, the robot can be out of camera's range of view.

[1]  Sung-Kwun Oh,et al.  A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation , 2013 .

[2]  M. Sahnoun,et al.  Assisted Control Mode for a Smart Wheelchair , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

[3]  Kang-Hyun Jo,et al.  Remote control of a moving robot using the virtual link , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[4]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[5]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[6]  Hee-Jun Kang,et al.  A visual-inertial servoing method for tracking object with two landmarks and an inertial measurement unit , 2011 .

[7]  Young Soo Suh,et al.  Pedestrian inertial navigation with gait phase detection assisted zero velocity updating , 2000, 2009 4th International Conference on Autonomous Robots and Agents.

[8]  Wan Kyun Chung,et al.  Control of car-like mobile robots for posture stabilization , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[9]  R. Jagadeesh Kannan,et al.  Temple and Maternity Ward Security using FPRS , 2013 .

[10]  G. Novak.  Roby-go, a prototype for several MiroSOT soccer playing robots , 2004, Second IEEE International Conference on Computational Cybernetics, 2004. ICCC 2004..

[11]  Jonghyuk Kim,et al.  Six DoF Decentralised SLAM , 2003 .

[12]  Liqiang Feng,et al.  Measurement and correction of systematic odometry errors in mobile robots , 1996, IEEE Trans. Robotics Autom..

[13]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[14]  Martin Buss,et al.  Visual tracking and control of a quadcopter using a stereo camera system and inertial sensors , 2009, 2009 International Conference on Mechatronics and Automation.

[15]  Xi Chen,et al.  Training special needs infants to drive mobile robots using force-feedback joystick , 2010, 2010 IEEE International Conference on Robotics and Automation.

[16]  泰義 横小路,et al.  IEEE International Conference on Robotics and Automation , 1992 .

[17]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  S. Mahlknecht,et al.  TINYPHOON A Tiny Autonomous Mobile Robot , 2005, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[19]  Norbert Link,et al.  Indoor 3D position estimation using low-cost inertial sensors and marker-based video-tracking , 2010, IEEE/ION Position, Location and Navigation Symposium.

[20]  Jin Bae Park,et al.  Adaptive observer-based trajectory tracking control of nonholonomic mobile robots , 2011 .